Industrial analytics and Artificial Intelligence (AI) is the application of data analytics to extract insights and valuable information from the large amounts of data generated by manufacturing processes. Industrial analytics optimizes and improves the manufacturing process by identifying areas for optimization, reduced waste, and increased efficiency. This can be achieved through the analysis of data from various sources such as sensors, machines and systems, and applying advanced analytics techniques such as predictive modeling, anomaly detection, and optimization algorithms.
Industrial analytics can also be used to gain insights into customer behavior, supply chain management, and quality control. By leveraging data-driven insights, manufacturers can make more informed decisions and gain a competitive edge in the market.
Today, the analytics market is extremely fluid. More companies are pursuing analytics solutions and more employees throughout the enterprise want more and better decision-making tools. And the increasing focus on Industry 4.0 (I4.0) and Industrial Internet of Things (IIoT) is driving demand for predictive maintenance solutions, which rely on advanced analytics.
All companies can benefit from enhanced insights into market demand, operations, inventory, and corporate performance. Analytics provide the information to effectively manage global markets, supply chains, and operations. Please visit market analysis page for recent ARC research related to Industrial analytics and Artificial Intelligence.
Artificial Intelligence Is Cited as the Most Impactful Technology
Industrial Artificial Intelligence (AI) is cited as the most impactful technologies for digital transformation of manufacturing industries. Artificial Intelligence relates to machines performing tasks that have typically required human intelligence, such as reasoning, problem-solving, and decision making. These systems hypothesize and formulate possible answers based on available evidence, can be trained through the ingestion of vast amounts of content, and automatically adapt and in some cases, learn from their mistakes and failures. Machine learning is a subset of AI involving the use of algorithms to learn from data and improve performance on a specific task without being explicitly programmed.
Advances in AI could not have come at a better time. Industrial manufacturing companies are constantly faced with a multitude of challenges, both current and future. As a result, manufacturers are increasingly focused on improving their business operations to ensure their short and long term viability.
However, implanting AI in manufacturing can be challenging, if not daunting, as it requires an understanding of business objectives and the integration of AI with existing processes and systems. This may explain why ARC primary research has revealed AI is cited as the most impactful technology over the next five years. If that is the case, then why do so many manufacturers also share stories of the collapse of their AI projects? For most companies, even the innovators, it is typically overwhelming to determine an effective starting point for any digital transformation project, including implementing AI.
Industrial AI Impact Assessment Model for People, Processes, and Technology
Industrial organizations can leverage ARC's Industrial AI Impact Assessment Model used by ARC's own team of Analysts, to guide their own AI evaluation and implementation process. This model offers a structured approach to assess the potential impact of AI on various aspects of the organization, including operations, strategy, and workforce.
As we continue to explore the potential of generative AI and other breakthroughs in industrial AI, collaboration and knowledge sharing become increasingly important. We invite you to join us in this journey, sharing your questions, experiences, learnings, and solutions.
The future of Industrial AI is promising, with its potential to transform industries and societal structures. By deepening our understanding and effectively applying AI technologies, we can unlock their true potential in the industrial realm.
Industrial AI's Role in Digital Transformation
Industrial AI, a subset of the broader field of artificial intelligence (AI), refers to the application of AI technologies (including Generative AI) in industrial settings to augment the workforce in pursuit of growth, profitability, more sustainable products and production processes, enhanced customer service, and business outcomes. Industrial AI leverages machine learning, deep learning, neural networks, and other approaches. Some of these techniques have been used for decades to build AI systems using data from various sources within an industrial environment, such as sensors, machinery, industrial engineers, and frontline workers.
The purpose of Industrial AI is multifold. It aims to enhance operational efficiency by automating repetitive tasks, improve accuracy by reducing human error, and enable real-time decision making based on data-driven insights. From generative design of products and production processes to intelligent production operations maintenance and quality control, to energy and supply chain optimization, efficient sales and enhanced customer service, Industrial AI finds its applications across a wide spectrum of industrial operations.
Industrial AI offers several benefits. It can significantly reduce operational costs by optimizing resource usage and improving process efficiency. By enabling predictive maintenance, it can minimize downtime and extend the lifespan of machinery. With its real-time decision-making capabilities, it allows for rapid response to changes in market demand or operational conditions, addressing organizational skills gaps, and enhancing agility and competitiveness.
However, the implementation of Industrial AI is not without risks. Ethical concerns arise from the potential misuse of AI technologies, while the automation of tasks may lead to job losses. Errors in AI algorithms could lead to faulty decisions with significant consequences, and the integration of AI into industrial systems poses security threats.
To mitigate these risks, robust governance frameworks should be established to ensure ethical and responsible use of AI. Measures should be taken to reskill and upskill workers affected by automation. Rigorous testing and validation procedures should be implemented to minimize errors in AI algorithms. Robust cybersecurity measures should be implemented to protect against threats.
In industrial organizations, achieving desired business outcomes requires a comprehensive approach that encompasses three crucial elements: people, processes, and technology. This framework is not just a buzz phrase, but a proven strategic blueprint that guides organizations toward sustainable success.
Build Business Case Consensus for Industrial AI
Applying AI in an industrial setting is a complex endeavor. To help you build internal consensus while avoiding unnecessary costs and false starts, ARC can provide answers to key questions, such as:
- What are the key specific business problems or challenges that industrial analytics can address?
- What roles and responsibilities are required?
- What data sources are available to ensure the data is accurate and relevant to the problems needed to be solves ?
- How are the results integrated into existing processes and systems to ensure the insights are actionable and can drive meaningful improvements?
- How to ensure analytic efforts are aligned with the overall strategic goal of the organization and are being used to drive meaningful outcomes for customers and stakeholders.